Polygenic height prediction for the Han Chinese in Taiwan

Chih Hao Chang; Che Yu Chou; Timothy G. Raben; Shih Ann Chen; Yuh Jyh Jong; Jeng Yih Wu; Shun Fa Yang; Hsiang Cheng Chen; Yen Lin Chen; Ming Chen; Gwo Chin Ma; Chih Yang Huang; Tso Fu Wang; Sing Lian Lee; Chen Fang Hung; See Tong Pang; Erik Widen; Yao Ming Chang; Erh Chan Yeh; Chun Yu Wei; Chien Hsiun Chen; Stephen D.H. Hsu; Pui Yan Kwok

doi:10.1038/s41525-025-00468-6

Polygenic height prediction for the Han Chinese in Taiwan

Chih Hao Chang, Che Yu Chou, Timothy G. Raben, Shih Ann Chen, Yuh Jyh Jong, Jeng Yih Wu, Shun Fa Yang, Hsiang Cheng Chen, Yen Lin Chen, Ming Chen, Gwo Chin Ma, Chih Yang Huang, Tso Fu Wang, Sing Lian Lee, Chen Fang Hung, See Tong Pang, Erik Widen, Yao Ming Chang, Erh Chan Yeh, Chun Yu WeiChien Hsiun Chen, Stephen D.H. Hsu, Pui Yan Kwok

Research output: Contribution to journal › Article › peer-review

Abstract

Human height prediction based on genetic factors alone shows positive correlation, but predictors developed for one population perform less well when applied to population of different ancestries. In this study, we evaluated the utility of incorporating non-genetic factors in height predictors for the Han Chinese population in Taiwan. We analyzed data from 78,719 Taiwan Biobank (TWB) participants and 40,641 Taiwan Precision Medicine Initiative (TPMI) participants using genome-wide association study and multivariable linear regression least absolute shrinkage and selection operator (LASSO) methods to incorporate genetic and non-genetic factors for height prediction. Our findings establish that combining birth year (as a surrogate for nutritional status), age at measurement (to account for age-associated effects on height), and genetic profile data improves the accuracy of height prediction. This method enhances the correlation between predicted and actual height and significantly reduces the discrepancies between predicted and actual height in both males and females.

Original language	English
Article number	7
Journal	npj Genomic Medicine
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41525-025-00468-6
Publication status	Published - Dec 2025
Externally published	Yes

ASJC Scopus subject areas

Molecular Biology
Genetics
Genetics(clinical)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41525-025-00468-6

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Chang, C. H., Chou, C. Y., Raben, T. G., Chen, S. A., Jong, Y. J., Wu, J. Y., Yang, S. F., Chen, H. C., Chen, Y. L., Chen, M., Ma, G. C., Huang, C. Y., Wang, T. F., Lee, S. L., Hung, C. F., Pang, S. T., Widen, E., Chang, Y. M., Yeh, E. C., ... Kwok, P. Y. (2025). Polygenic height prediction for the Han Chinese in Taiwan. npj Genomic Medicine, 10(1), Article 7. https://doi.org/10.1038/s41525-025-00468-6

Chang, CH, Chou, CY, Raben, TG, Chen, SA, Jong, YJ, Wu, JY, Yang, SF, Chen, HC, Chen, YL, Chen, M, Ma, GC, Huang, CY, Wang, TF, Lee, SL, Hung, CF, Pang, ST, Widen, E, Chang, YM, Yeh, EC, Wei, CY, Chen, CH, Hsu, SDH & Kwok, PY 2025, 'Polygenic height prediction for the Han Chinese in Taiwan', npj Genomic Medicine, vol. 10, no. 1, 7. https://doi.org/10.1038/s41525-025-00468-6

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title = "Polygenic height prediction for the Han Chinese in Taiwan",

abstract = "Human height prediction based on genetic factors alone shows positive correlation, but predictors developed for one population perform less well when applied to population of different ancestries. In this study, we evaluated the utility of incorporating non-genetic factors in height predictors for the Han Chinese population in Taiwan. We analyzed data from 78,719 Taiwan Biobank (TWB) participants and 40,641 Taiwan Precision Medicine Initiative (TPMI) participants using genome-wide association study and multivariable linear regression least absolute shrinkage and selection operator (LASSO) methods to incorporate genetic and non-genetic factors for height prediction. Our findings establish that combining birth year (as a surrogate for nutritional status), age at measurement (to account for age-associated effects on height), and genetic profile data improves the accuracy of height prediction. This method enhances the correlation between predicted and actual height and significantly reduces the discrepancies between predicted and actual height in both males and females.",

author = "Chang, {Chih Hao} and Chou, {Che Yu} and Raben, {Timothy G.} and Chen, {Shih Ann} and Jong, {Yuh Jyh} and Wu, {Jeng Yih} and Yang, {Shun Fa} and Chen, {Hsiang Cheng} and Chen, {Yen Lin} and Ming Chen and Ma, {Gwo Chin} and Huang, {Chih Yang} and Wang, {Tso Fu} and Lee, {Sing Lian} and Hung, {Chen Fang} and Pang, {See Tong} and Erik Widen and Chang, {Yao Ming} and Yeh, {Erh Chan} and Wei, {Chun Yu} and Chen, {Chien Hsiun} and Hsu, {Stephen D.H.} and Kwok, {Pui Yan}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",

year = "2025",

month = dec,

doi = "10.1038/s41525-025-00468-6",

language = "English",

volume = "10",

journal = "npj Genomic Medicine",

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T1 - Polygenic height prediction for the Han Chinese in Taiwan

AU - Chang, Chih Hao

AU - Chou, Che Yu

AU - Raben, Timothy G.

AU - Chen, Shih Ann

AU - Jong, Yuh Jyh

AU - Wu, Jeng Yih

AU - Yang, Shun Fa

AU - Chen, Hsiang Cheng

AU - Chen, Yen Lin

AU - Chen, Ming

AU - Ma, Gwo Chin

AU - Huang, Chih Yang

AU - Wang, Tso Fu

AU - Lee, Sing Lian

AU - Hung, Chen Fang

AU - Pang, See Tong

AU - Widen, Erik

AU - Chang, Yao Ming

AU - Yeh, Erh Chan

AU - Wei, Chun Yu

AU - Chen, Chien Hsiun

AU - Hsu, Stephen D.H.

AU - Kwok, Pui Yan

PY - 2025/12

Y1 - 2025/12

N2 - Human height prediction based on genetic factors alone shows positive correlation, but predictors developed for one population perform less well when applied to population of different ancestries. In this study, we evaluated the utility of incorporating non-genetic factors in height predictors for the Han Chinese population in Taiwan. We analyzed data from 78,719 Taiwan Biobank (TWB) participants and 40,641 Taiwan Precision Medicine Initiative (TPMI) participants using genome-wide association study and multivariable linear regression least absolute shrinkage and selection operator (LASSO) methods to incorporate genetic and non-genetic factors for height prediction. Our findings establish that combining birth year (as a surrogate for nutritional status), age at measurement (to account for age-associated effects on height), and genetic profile data improves the accuracy of height prediction. This method enhances the correlation between predicted and actual height and significantly reduces the discrepancies between predicted and actual height in both males and females.

AB - Human height prediction based on genetic factors alone shows positive correlation, but predictors developed for one population perform less well when applied to population of different ancestries. In this study, we evaluated the utility of incorporating non-genetic factors in height predictors for the Han Chinese population in Taiwan. We analyzed data from 78,719 Taiwan Biobank (TWB) participants and 40,641 Taiwan Precision Medicine Initiative (TPMI) participants using genome-wide association study and multivariable linear regression least absolute shrinkage and selection operator (LASSO) methods to incorporate genetic and non-genetic factors for height prediction. Our findings establish that combining birth year (as a surrogate for nutritional status), age at measurement (to account for age-associated effects on height), and genetic profile data improves the accuracy of height prediction. This method enhances the correlation between predicted and actual height and significantly reduces the discrepancies between predicted and actual height in both males and females.

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Polygenic height prediction for the Han Chinese in Taiwan

Abstract

ASJC Scopus subject areas

UN SDGs

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